Functional genomics with microarrays in fish biology and fisheries

Authors

  • Frederick William Goetz,

    1. Great Lakes WATER Institute, University of Wisconsin – Milwaukee, 600 E. Greenfield Ave., Milwaukee, WI 53204, USA
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  • Simon MacKenzie

    1. Departament de Biología Cel.lular, Fisiologia i Immunologia, Facultat de Biociencies, Unitat de Fisiologia Animal, Universitat Autonoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
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Frederick William Goetz, Great Lakes WATER Institute, University of Wisconsin – Milwaukee, 600 E. Greenfield Ave., Milwaukee, WI 53204, USA
Tel.: +1 414-382-1742
Fax: +1 414-382-1705
E-mail: rick@uwm.edu

Abstract

Over the past 10 years, DNA microarrays have been used for the analysis of gene expression during various physiological, developmental or cellular processes in fish. However, in the last few years, investigators have begun to use microarrays on fish to address ecological, evolutionary and environmental questions including the variability of gene expression in natural populations, speciation, ecotypic diversity, environmental remediation and host–pathogen interactions. Given their commercial importance and the availability of several microarray platforms, most of the research in these areas has focused on salmonids. However, the same issues could be studied in a number of fish species of interest to world fisheries. The application of array technology to analyse gene expression in exploited species may require the development of new array platforms containing genes derived specifically from these fish. The gene discovery required for developing these platforms will certainly be facilitated by the new sequencing technologies that have recently been developed. Further, given the quantum leap that has occurred in sequencing, and the likely improvements in these technologies in the immediate future, it may be possible in certain situations to use sequencing in place of arrays to measure global changes in gene expression. Given the current technological development, sequencing has an advantage over arrays in that it can be used as a tool for gene discovery as well as for quantifying gene expression.

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